The ability to automatically monitor and manage dynamic situations is needed in many application domains. Examples include management of real-time environments such as tactical battlefields, public safety and health-care systems during disaster response, trading in global financial markets, services provided by large-scale telecommunications networks, homeland defense, industrial infrastructure security, and urban infrastructure security. These domains involve a large number of objects that change their states in time and space, and these objects may involve each other in complex spatio-temporal, structural, connectivity, causal, and domain-specific relations. These domains also may involve a large number of informational objects which are stored in databases or distributed as web content. A large amount of information is collected about the objects using sources such as notifications from remote equipment, sensors, probes, surveillance equipment, distributed networked data processing systems, and reports from field personnel, intelligence information, news media, and other observers. This information is collected over one or more data networks, typically in large volumes, for possible storage and for automated analysis using signal and data processing equipment.
From the management viewpoint it is important that the highest priority or most relevant situations be immediately and continuously viewable. Furthermore it must be possible for personnel who view the priority or most relevant situations to see the context and attributes of the situations, to view emerging trends and potential threats or risks, and to initiate actions that lead to preferred or safe situations.
There are a number of approaches available for monitoring and management of these types of application domains. These approaches typically are implemented in software on networked data processing systems which are used to continuously monitor or manage the domain. Information and data are presented on one or more data processing displays located in a network operations center, situation room, military command center, public safety operations center, infrastructure security operations control center, or financial trading center, and may be presented to roaming, mobile or remote personnel as well. The user interface of the data processing displays are used by monitoring and management personnel to view information and possibly view context, trends, and historical graphs, and initiate discovery or response actions.
In fault management of telecommunications networks, information is continuously collected from network elements and is combined with data processing models of the equipment and the topology of the network to identify failures to operations technicians. The information may be pre-processed using event correlation before combining it with data processing models of the network equipment and network topology. Event correlation (EC) may do root-cause analysis by distinguishing independent and dependent events, eliminating redundant events, and may synthesize events that are inferred from existing events. The models in this domain are typically static and include description of the operational characteristics of network equipment and network services. Further as conventionally practiced, existing fault management systems cannot readily relate network fault situations with other situations that depend on the telecommunications network in some way. Types of related situations include network performance degradation situations, violations of service level agreements situations, intrusion situations, traffic congestion situations, business processes situations that are operated using the telecommunications network and industrial or urban infrastructure failures that are interdependent with the telecommunications network operation. These limitations are due to the limitations in the fault management systems with respect to knowledge of situations, representation of situations, sharing knowledge of situations, and reasoning about situations.
In financial trading for fund management, information is collected continuously using data processing networks and systems about securities, interest rates, financial market conditions, analysts' reports, mergers and acquisitions, regulatory changes, company announcements and filings, and external events. This information is combined with information about portfolios, funds, and customers, and presented to fund managers, research analysts and traders. As conventionally practiced there may be data processing models and automated data processing analysis to identify fund trends, trading opportunities and recommendations. There may be one or more trading rooms with data processing displays and user interfaces in which traders, analysts, and fund managers view current information related to fund management and security transactions. As conventionally practiced, existing fund management systems can not automatically relate fund situations with other situations that impact fund management. Such other situations include analyst briefings by corporate executives, regulatory changes in an industry covered by the fund, and acquisitions by companies in the fund. These limitations are due to the limitations in the fund management systems with respect to knowledge of situations, representation of situations, and reasoning about situations.
In battlespace management, for example, in a tactical land and air battlespace, information is collected from a variety of real-time sources including sensors on field equipment, air surveillance, field personnel, satellite, and ground radar. This information is collected using a variety of data processing networks and systems, and is combined with information and models about terrain, weather conditions, operational unit formation and size, vehicles, aircraft, weapon systems, and battle plans. There may be one or more situation rooms or military command centers with data processing displays and user interfaces in which commanders view current information related to the battle space. As conventionally practiced under the paradigm of Network Centric Warfare (NCW) to obtain information superiority, according to a recent US Air Force solicitation [Air Force SBIR FY05. 1], the “amount of data continues to grow exponentially while the information is lost” with limited ability to assist in overall situation awareness and understanding. These limitations are due to the limitations in the battlefield management systems with respect to knowledge of situations, representation of situations, and reasoning about situations.
In autonomic systems, information is continuously collected from sensors and is combined with data processing models and the topology of a domain. An autonomic system is collectively understood as (i) self-organized control in mobile robotic devices and platforms or a collection of mobile robotic devices and platforms engaged in chemically, biologically, and radioactively aggressive environments; (ii) autonomic self-organized and self-healing functions of networked micro-sensing devices, and (iii) autonomic and adaptable self-control in remote transportation and scientific platforms and devices for atmospheric, oceanic, and planetary explorations. Further as conventionally practiced, existing autonomic systems can not readily relate domain situations with other situations. These limitations are due to the limitations of autonomic systems with respect to knowledge of situations, representation of situations, and reasoning about situations.